code: def check_support_multiagent(alg: str, config: AlgorithmConfig):
      register_env(
          "multi_agent_mountaincar",
          lambda _: MultiAgentMountainCar({"num_agents": 2}),
      )
      register_env(
          "multi_agent_cartpole",
          lambda _: MultiAgentCartPole({"num_agents": 2}),
      )
  
      # Simulate a simple multi-agent setup.
      policies = {
          "policy_0": PolicySpec(config={"gamma": 0.99}),
          "policy_1": PolicySpec(config={"gamma": 0.95}),
      }
      policy_ids = list(policies.keys())
  
      def policy_mapping_fn(agent_id, episode, worker, **kwargs):
          pol_id = policy_ids[agent_id]
          return pol_id
  
      config.multi_agent(
          policies=policies, policy_mapping_fn=policy_mapping_fn
      )
  
      for fw in framework_iterator(config):
          if fw == "tf2" and alg == "IMPALA":
              continue
          if alg == "SAC":
              a = config.build(env="multi_agent_mountaincar")
          else:
              a = config.build(env="multi_agent_cartpole")
  
          results = a.train()
          check_train_results(results)
          print(results)
          a.stop()
  
imports: import unittest
  import ray
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.dqn import DQNConfig
  from ray.rllib.algorithms.impala import ImpalaConfig
  from ray.rllib.algorithms.ppo import PPOConfig
  from ray.rllib.algorithms.sac import SACConfig
  from ray.rllib.examples.env.multi_agent import MultiAgentCartPole, MultiAgentMountainCar
  from ray.rllib.policy.policy import PolicySpec
  from ray.rllib.utils.test_utils import check_train_results, framework_iterator
  from ray.tune.registry import register_env
label_desc: Use image node_id 1 for calling a global function with example usage: check_support_multiagent(alg, config) and returns: pol_id